Project Summary/Abstract: In the post genome era, biological research and genomic medicine have been transformed by high-throughput technologies. New techniques have enabled researchers to investigate biological systems in great detail. Nonetheless, the extraordinary amount of information in the large number of emerging high-dimension datasets has not been fully exploited. Increasingly, pathway analysis and other a priori biological knowledge based approaches have improved success in extraction of valuable information from high-throughput experiments and genome-wide association studies. Preeclampsia is a complex disease and one of the most common causes of fetal and maternal morbidity and mortality worldwide. It is one of the great but enigmatic health problems. Despite many studies, there has been little fundamental improvement in our understanding in decades. It is a multi-system hypertensive disorder of pregnancy, characterized by variable degrees of maternal symptoms including elevated blood pressure, proteinuria and fetal growth retardation that affects 2-8 % of deliveries in the US. Many clinicians believe there is a difference between mild and severe or early and late preeclampsia. However, to date there is little direct evidence that they represent different genetic ideologies. We hypothesize that preeclampsia is a complex, polygenic disorder that entails activation of a network of genes. We further hypothesize that rare variants in the genes that contribute to the risk of preeclampsia can be identified using new bioinformatic approaches coupled with high-throughput technologies applied to appropriate cohorts of patients. We propose novel computational approaches to identify relevant genes and high-throughput technologies on appropriately selected patients that will help to identify the genetic architecture of this a multifactorial, polygenic disease.